#' Missing Data

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Substitute missing data with a string or factor level.

#'

#' @param x (`factor` or `character` vector)\cr values for which any missing values should be substituted.

#' @param label (`character`)\cr string that missing data should be replaced with.

#'

#' @return `x` with any `NA` values substituted by `label`.

#'

#' @examples

#' explicit_na(c(NA, "a", "b"))

#' is.na(explicit_na(c(NA, "a", "b")))

#'

#' explicit_na(factor(c(NA, "a", "b")))

#' is.na(explicit_na(factor(c(NA, "a", "b"))))

#'

#' explicit_na(sas_na(c("a", "")))

#'

#' @export

explicit_na <- function(x, label = "<Missing>") {

  } else {

  }

}

#' Convert Strings to `NA`

#'

#' @description `r lifecycle::badge("stable")`

#'

#' SAS imports missing data as empty strings or strings with whitespaces only. This helper function can be used to

#' convert these values to `NA`s.

#'

#' @inheritParams explicit_na

#' @param empty (`logical`)\cr if `TRUE` empty strings get replaced by `NA`.

#' @param whitespaces (`logical`)\cr if `TRUE` then strings made from whitespaces only get replaced with `NA`.

#'

#' @return `x` with `""` and/or whitespace-only values substituted by `NA`, depending on the values of

#'   `empty` and `whitespaces`.

#'

#' @examples

#' sas_na(c("1", "", " ", "   ", "b"))

#' sas_na(factor(c("", " ", "b")))

#'

#' is.na(sas_na(c("1", "", " ", "   ", "b")))

#'

#' @export

sas_na <- function(x, empty = TRUE, whitespaces = TRUE) {

  } else {

  }

}

#' Pairwise CoxPH model

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Summarize p-value, HR and CIs from stratified or unstratified CoxPH model.

#'

#' @inheritParams argument_convention

#' @inheritParams s_surv_time

#' @param strat (`character` or `NULL`)\cr variable names indicating stratification factors.

#' @param control (`list`)\cr parameters for comparison details, specified by using the helper function

#'   [control_coxph()]. Some possible parameter options are:

#'   * `pval_method` (`string`)\cr p-value method for testing hazard ratio = 1. Default method is "log-rank" which

#'     comes from [survival::survdiff()], can also be set to "wald" or "likelihood" (from [survival::coxph()]).

#'   * `ties` (`string`)\cr specifying the method for tie handling. Default is "efron",

#'     can also be set to "breslow" or "exact". See more in [survival::coxph()]

#'   * `conf_level` (`proportion`)\cr confidence level of the interval for HR.

#'

#' @name survival_coxph_pairwise

NULL

#' @describeIn survival_coxph_pairwise Statistics function which analyzes HR, CIs of HR and p-value of a coxph model.

#'

#' @return

#' * `s_coxph_pairwise()` returns the statistics:

#'   * `pvalue`: p-value to test HR = 1.

#'   * `hr`: Hazard ratio.

#'   * `hr_ci`: Confidence interval for hazard ratio.

#'   * `n_tot`: Total number of observations.

#'   * `n_tot_events`: Total number of events.

#'

#' @examples

#' library(dplyr)

#'

#' adtte_f <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   mutate(is_event = CNSR == 0)

#' df <- adtte_f %>%

#'   filter(ARMCD == "ARM A")

#' df_ref_group <- adtte_f %>%

#'   filter(ARMCD == "ARM B")

#'

#' # Internal function - s_coxph_pairwise

#' \dontrun{

#' s_coxph_pairwise(df, df_ref_group, .in_ref_col = FALSE, .var = "AVAL", is_event = "is_event")

#' }

#'

#' @keywords internal

s_coxph_pairwise <- function(df,

                             .ref_group,

                             .in_ref_col,

                             .var,

                             is_event,

                             strat = NULL,

                             control = control_coxph()) {

      )

    )

  }

  )

  } else {

        ")"

      )

    )

  }

  )

  )

  )

  )

}

#' @describeIn survival_coxph_pairwise Formatted analysis function which is used as `afun` in `coxph_pairwise()`.

#'

#' @return

#' * `a_coxph_pairwise()` returns the corresponding list with formatted [rtables::CellValue()].

#'

#' @examples

#' # Internal function - a_coxph_pairwise

#' \dontrun{

#' a_coxph_pairwise(df, df_ref_group, .in_ref_col = FALSE, .var = "AVAL", is_event = "is_event")

#' }

#'

#' @keywords internal

a_coxph_pairwise <- make_afun(

  s_coxph_pairwise,

  .indent_mods = c(pvalue = 0L, hr = 0L, hr_ci = 1L, n_tot = 0L, n_tot_events = 0L),

  .formats = c(

    pvalue = "x.xxxx | (<0.0001)",

    hr = "xx.xx",

    hr_ci = "(xx.xx, xx.xx)",

    n_tot = "xx.xx",

    n_tot_events = "xx.xx"

  )

)

#' @describeIn survival_coxph_pairwise Layout-creating function which can take statistics function arguments

#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].

#'

#' @return

#' * `coxph_pairwise()` returns a layout object suitable for passing to further layouting functions,

#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing

#'   the statistics from `s_coxph_pairwise()` to the table layout.

#'

#' @examples

#' basic_table() %>%

#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%

#'   add_colcounts() %>%

#'   coxph_pairwise(

#'     vars = "AVAL",

#'     is_event = "is_event",

#'     var_labels = "Unstratified Analysis"

#'   ) %>%

#'   build_table(df = adtte_f)

#'

#' basic_table() %>%

#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%

#'   add_colcounts() %>%

#'   coxph_pairwise(

#'     vars = "AVAL",

#'     is_event = "is_event",

#'     var_labels = "Stratified Analysis",

#'     strat = "SEX",

#'     control = control_coxph(pval_method = "wald")

#'   ) %>%

#'   build_table(df = adtte_f)

#'

#' @export

coxph_pairwise <- function(lyt,

                           vars,

                           ...,

                           var_labels = "CoxPH",

                           show_labels = "visible",

                           table_names = vars,

                           .stats = c("pvalue", "hr", "hr_ci"),

                           .formats = NULL,

                           .labels = NULL,

                           .indent_mods = NULL) {

  )

  )

}

#' Summary numeric variables in columns

#'

#' @description `r lifecycle::badge("experimental")`

#'

#' Layout-creating function which can be used for creating column-wise summary tables.

#' This function sets the analysis methods as column labels and is a wrapper for

#' [rtables::analyze_colvars()]. It was designed principally for PK tables.

#'

#' @inheritParams argument_convention

#' @inheritParams rtables::analyze_colvars

#' @param row_labels (`character`)\cr as this function works in columns space, usual `.labels`

#'   character vector applies on the column space. You can change the row labels by defining this

#'   parameter to a named character vector with names corresponding to the split values. It defaults

#'   to `NULL` and if it contains only one `string`, it will duplicate that as a row label.

#' @param do_summarize_row_groups (`flag`)\cr defaults to `FALSE` and applies the analysis to the current

#'   label rows. This is a wrapper of [rtables::summarize_row_groups()] and it can accept `labelstr`

#'   to define row labels. This behavior is not supported as we never need to overload row labels.

#' @param split_col_vars (`flag`)\cr defaults to `TRUE` and puts the analysis results onto the columns.

#'   This option allows you to add multiple instances of this functions, also in a nested fashion,

#'   without adding more splits. This split must happen only one time on a single layout.

#'

#' @return

#' A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].

#' Adding this function to an `rtable` layout will summarize the given variables, arrange the output

#' in columns, and add it to the table layout.

#'

#' @note This is an experimental implementation of [rtables::summarize_row_groups()] and

#'   [rtables::analyze_colvars()] that may be subjected to changes as `rtables` extends its

#'   support to more complex analysis pipelines on the column space. For the same reasons,

#'   we encourage to read the examples carefully and file issues for cases that differ from

#'   them.

#'

#'   Here `labelstr` behaves differently than usual. If it is not defined (default as `NULL`),

#'   row labels are assigned automatically to the split values in case of `rtables::analyze_colvars`

#'   (`do_summarize_row_groups = FALSE`, the default), and to the group label for

#'   `do_summarize_row_groups = TRUE`.

#'

#' @seealso [summarize_vars()], [rtables::analyze_colvars()].

#'

#' @examples

#' library(dplyr)

#'

#' # Data preparation

#' adpp <- tern_ex_adpp %>% h_pkparam_sort()

#'

#' lyt <- basic_table() %>%

#'   split_rows_by(var = "STRATA1", label_pos = "topleft") %>%

#'   split_rows_by(

#'     var = "SEX",

#'     label_pos = "topleft",

#'     child_label = "hidden"

#'   ) %>% # Removes duplicated labels

#'   analyze_vars_in_cols(vars = "AGE")

#' result <- build_table(lyt = lyt, df = adpp)

#' result

#'

#' # By selecting just some statistics and ad-hoc labels

#' lyt <- basic_table() %>%

#'   split_rows_by(var = "ARM", label_pos = "topleft") %>%

#'   split_rows_by(

#'     var = "SEX",

#'     label_pos = "topleft",

#'     child_labels = "hidden",

#'     split_fun = drop_split_levels

#'   ) %>%

#'   analyze_vars_in_cols(

#'     vars = "AGE",

#'     .stats = c("n", "cv", "geom_mean"),

#'     .labels = c(

#'       n = "aN",

#'       cv = "aCV",

#'       geom_mean = "aGeomMean"

#'     )

#'   )

#' result <- build_table(lyt = lyt, df = adpp)

#' result

#'

#' # Changing row labels

#' lyt <- basic_table() %>%

#'   analyze_vars_in_cols(

#'     vars = "AGE",

#'     row_labels = "some custom label"

#'   )

#' result <- build_table(lyt, df = adpp)

#' result

#'

#' # Pharmacokinetic parameters

#' lyt <- basic_table() %>%

#'   split_rows_by(

#'     var = "TLG_DISPLAY",

#'     split_label = "PK Parameter",

#'     label_pos = "topleft",

#'     child_label = "hidden"

#'   ) %>%

#'   analyze_vars_in_cols(

#'     vars = "AVAL"

#'   )

#' result <- build_table(lyt, df = adpp)

#' result

#'

#' # Multiple calls (summarize label and analyze underneath)

#' lyt <- basic_table() %>%

#'   split_rows_by(

#'     var = "TLG_DISPLAY",

#'     split_label = "PK Parameter",

#'     label_pos = "topleft"

#'   ) %>%

#'   analyze_vars_in_cols(

#'     vars = "AVAL",

#'     do_summarize_row_groups = TRUE # does a summarize level

#'   ) %>%

#'   split_rows_by("SEX",

#'     child_label = "hidden",

#'     label_pos = "topleft"

#'   ) %>%

#'   analyze_vars_in_cols(

#'     vars = "AVAL",

#'     split_col_vars = FALSE # avoids re-splitting the columns

#'   )

#' result <- build_table(lyt, df = adpp)

#' result

#'

#' @export

analyze_vars_in_cols <- function(lyt,

                                 vars,

                                 ...,

                                 .stats = c(

                                   "n",

                                   "mean",

                                   "sd",

                                   "se",

                                   "cv",

                                   "geom_cv"

                                 ),

                                 .labels = c(

                                   n = "n",

                                   mean = "Mean",

                                   sd = "SD",

                                   se = "SE",

                                   cv = "CV (%)",

                                   geom_cv = "CV % Geometric Mean"

                                 ),

                                 row_labels = NULL,

                                 do_summarize_row_groups = FALSE,

                                 split_col_vars = TRUE,

                                 .indent_mods = NULL,

                                 nested = TRUE,

                                 na_level = NULL,

                                 .formats = NULL) {

  # Automatic assignment of formats

    # General values

  } else {

  }

  # Check for vars in the case that one or more are used

    )

  }

    # Checking there is not a previous identical column split

    )

      )

    }

    # Main col split

    )

  }

    }

    # Function list for do_summarize_row_groups. Slightly different handling of labels

          # Statistic

          # Label check and replacement

              )

            }

          } else {

          }

          # Cell creation

          )

        }

      },

    )

    # Main call to rtables

    )

  } else {

    # Function list for analyze_colvars

          # Main statistics

          # Label from context

          # Label switcher

          } else {

                )

              }

            } else {

            }

          }

          # Cell creation

          )

        }

      },

    )

    # Main call to rtables

    )

  }

}

# Help function

get_last_col_split <- function(lyt) {

}

#' Cox Proportional Hazards Regression

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Fits a Cox regression model and estimates hazard ratio to describe the effect size in a survival analysis.

#'

#' @inheritParams argument_convention

#'

#' @details Cox models are the most commonly used methods to estimate the magnitude of

#'   the effect in survival analysis. It assumes proportional hazards: the ratio

#'   of the hazards between groups (e.g., two arms) is constant over time.

#'   This ratio is referred to as the "hazard ratio" (HR) and is one of the

#'   most commonly reported metrics to describe the effect size in survival

#'   analysis (NEST Team, 2020).

#'

#' @seealso [fit_coxreg] for relevant fitting functions, [h_cox_regression] for relevant

#'   helper functions, and [tidy_coxreg] for custom tidy methods.

#'

#' @examples

#' library(survival)

#'

#' # Testing dataset [survival::bladder].

#' set.seed(1, kind = "Mersenne-Twister")

#' dta_bladder <- with(

#'   data = bladder[bladder$enum < 5, ],

#'   tibble::tibble(

#'     TIME = stop,

#'     STATUS = event,

#'     ARM = as.factor(rx),

#'     COVAR1 = as.factor(enum) %>% formatters::with_label("A Covariate Label"),

#'     COVAR2 = factor(

#'       sample(as.factor(enum)),

#'       levels = 1:4, labels = c("F", "F", "M", "M")

#'     ) %>% formatters::with_label("Sex (F/M)")

#'   )

#' )

#' dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)

#' dta_bladder$STUDYID <- factor("X")

#'

#' plot(

#'   survfit(Surv(TIME, STATUS) ~ ARM + COVAR1, data = dta_bladder),

#'   lty = 2:4,

#'   xlab = "Months",

#'   col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")

#' )

#'

#' @name cox_regression

NULL

#' @describeIn cox_regression Statistics function that transforms results tabulated

#'   from [fit_coxreg_univar()] or [fit_coxreg_multivar()] into a list.

#'

#' @param model_df (`data.frame`)\cr contains the resulting model fit from a [fit_coxreg]

#'   function with tidying applied via [broom::tidy()].

#' @param .stats (`character`)\cr the name of statistics to be reported among:

#'   * `n`: number of observations (univariate only)

#'   * `hr`: hazard ratio

#'   * `ci`: confidence interval

#'   * `pval`: p-value of the treatment effect

#'   * `pval_inter`: p-value of the interaction effect between the treatment and the covariate (univariate only)

#' @param .which_vars (`character`)\cr which rows should statistics be returned for from the given model.

#'   Defaults to "all". Other options include "var_main" for main effects, "inter" for interaction effects,

#'   and "multi_lvl" for multivariate model covariate level rows. When `.which_vars` is "all" specific

#'   variables can be selected by specifying `.var_nms`.

#' @param .var_nms (`character`)\cr the `term` value of rows in `df` for which `.stats` should be returned. Typically

#'   this is the name of a variable. If using variable labels, `var` should be a vector of both the desired

#'   variable name and the variable label in that order to see all `.stats` related to that variable. When `.which_vars`

#'   is "var_main" `.var_nms` should be only the variable name.

#'

#' @return

#' * `s_coxreg()` returns the selected statistic for from the Cox regression model for the selected variable(s).

#'

#' @examples

#' # s_coxreg

#'

#' # Univariate

#' u1_variables <- list(

#'   time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")

#' )

#' univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder)

#' df1 <- broom::tidy(univar_model)

#' s_coxreg(model_df = df1, .stats = "hr")

#'

#' # Univariate with interactions

#' univar_model_inter <- fit_coxreg_univar(

#'   variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder

#' )

#' df1_inter <- broom::tidy(univar_model_inter)

#' s_coxreg(model_df = df1_inter, .stats = "hr", .which_vars = "inter", .var_nms = "COVAR1")

#'

#' # Univariate without treatment arm - only "COVAR2" covariate effects

#' u2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))

#' univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder)

#' df1_covs <- broom::tidy(univar_covs_model)

#' s_coxreg(model_df = df1_covs, .stats = "hr", .var_nms = c("COVAR2", "Sex (F/M)"))

#'

#' # Multivariate.

#' m1_variables <- list(

#'   time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")

#' )

#' multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder)

#' df2 <- broom::tidy(multivar_model)

#' s_coxreg(model_df = df2, .stats = "pval", .which_vars = "var_main", .var_nms = "COVAR1")

#' s_coxreg(

#'   model_df = df2, .stats = "pval", .which_vars = "multi_lvl",

#'   .var_nms = c("COVAR1", "A Covariate Label")

#' )

#'

#' # Multivariate without treatment arm - only "COVAR1" main effect

#' m2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))

#' multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder)

#' df2_covs <- broom::tidy(multivar_covs_model)

#' s_coxreg(model_df = df2_covs, .stats = "hr")

#'

#' @export

s_coxreg <- function(model_df, .stats, .which_vars = "all", .var_nms = NULL) {

  # We need a list with names corresponding to the stats to display of equal length to the list of stats.

  }

    }

  )

}

#' @describeIn cox_regression Analysis function which is used as `afun` in [rtables::analyze()]

#'   and `cfun` in [rtables::summarize_row_groups()] within `summarize_coxreg()`.

#'

#' @param eff (`flag`)\cr whether treatment effect should be calculated. Defaults to `FALSE`.

#' @param var_main (`flag`)\cr whether main effects should be calculated. Defaults to `FALSE`.

#' @param na_level (`string`)\cr custom string to replace all `NA` values with. Defaults to `""`.

#' @param cache_env (`environment`)\cr an environment object used to cache the regression model in order to

#'   avoid repeatedly fitting the same model for every row in the table. Defaults to `NULL` (no caching).

#'

#' @return

#' * `a_coxreg()` returns formatted [rtables::CellValue()].

#'

#' @examples

#' tern:::a_coxreg(

#'   df = dta_bladder,

#'   labelstr = "Label 1",

#'   variables = u1_variables,

#'   .spl_context = list(value = "COVAR1"),

#'   .stats = "n",

#'   .formats = "xx"

#' )

#'

#' tern:::a_coxreg(

#'   df = dta_bladder,

#'   labelstr = "",

#'   variables = u1_variables,

#'   .spl_context = list(value = "COVAR2"),

#'   .stats = "pval",

#'   .formats = "xx.xxxx"

#' )

#'

#' @keywords internal

a_coxreg <- function(df,

                     labelstr,

                     eff = FALSE,

                     var_main = FALSE,

                     multivar = FALSE,

                     variables,

                     at = list(),

                     control = control_coxreg(),

                     .spl_context,

                     .stats,

                     .formats,

                     na_level = "",

                     cache_env = NULL) {

  } else {

  }

    } else {

    }

  } else {

  }

  }

    } else { # treatment effect

    }

  } else {

    }

  }

  } else {

  }

  )

}

#' @describeIn cox_regression Layout-creating function which creates a Cox regression summary table

#'   layout. This function is a wrapper for several `rtables` layouting functions. This function

#'   is a wrapper for [rtables::analyze_colvars()] and [rtables::summarize_row_groups()].

#'

#' @inheritParams fit_coxreg_univar

#' @param multivar (`flag`)\cr Defaults to `FALSE`. If `TRUE` multivariate Cox regression will run, otherwise

#'   univariate Cox regression will run.

#' @param common_var (`character`)\cr the name of a factor variable in the dataset which takes the same value

#'   for all rows. This should be created during pre-processing if no such variable currently exists.

#' @param .section_div (`character`)\cr string which should be repeated as a section divider between sections.

#'   Defaults to `NA` for no section divider. If a vector of two strings are given, the first will be used between

#'   treatment and covariate sections and the second between different covariates.

#'

#' @return

#' * `summarize_coxreg()` returns a layout object suitable for passing to further layouting functions,

#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add a Cox regression table

#'   containing the chosen statistics to the table layout.

#'

#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()] which also take the `variables`, `data`,

#'   `at` (univariate only), and `control` arguments but return unformatted univariate and multivariate

#'   Cox regression models, respectively.

#'

#' @examples

#' # summarize_coxreg

#'

#' result_univar <- basic_table() %>%

#'   summarize_coxreg(variables = u1_variables) %>%

#'   build_table(dta_bladder)

#' result_univar

#'

#' result_multivar <- basic_table() %>%

#'   summarize_coxreg(

#'     variables = m1_variables,

#'     multivar = TRUE,

#'   ) %>%

#'   build_table(dta_bladder)

#' result_multivar

#'

#' result_univar_covs <- basic_table() %>%

#'   summarize_coxreg(

#'     variables = u2_variables,

#'   ) %>%

#'   build_table(dta_bladder)

#' result_univar_covs

#'

#' result_multivar_covs <- basic_table() %>%

#'   summarize_coxreg(

#'     variables = m2_variables,

#'     multivar = TRUE,

#'     varlabels = c("Covariate 1", "Covariate 2") # custom labels

#'   ) %>%

#'   build_table(dta_bladder)

#' result_multivar_covs

#'

#' @export

summarize_coxreg <- function(lyt,

                             variables,

                             control = control_coxreg(),

                             at = list(),

                             multivar = FALSE,

                             common_var = "STUDYID",

                             .stats = c("n", "hr", "ci", "pval", "pval_inter"),

                             .formats = c(

                               n = "xx", hr = "xx.xx", ci = "(xx.xx, xx.xx)",

                               pval = "x.xxxx | (<0.0001)", pval_inter = "x.xxxx | (<0.0001)"

                             ),

                             varlabels = NULL,

                             .indent_mods = NULL,

                             na_level = "",

                             .section_div = NA_character_) {

    ))

  }

  }

  } else {

  }

  )

      )

    )

      ) %>%

        )

      )

        )

    }

  }

      ) %>%

        )

      )

        )

    }

  }

}

#' Helper Functions for Multivariate Logistic Regression

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Helper functions used in calculations for logistic regression.

#'

#' @inheritParams argument_convention

#' @param fit_glm (`glm`)\cr logistic regression model fitted by [stats::glm()] with "binomial" family.

#'   Limited functionality is also available for conditional logistic regression models fitted by

#'   [survival::clogit()], currently this is used only by [extract_rsp_biomarkers()].

#' @param x (`string` or `character`)\cr a variable or interaction term in `fit_glm` (depending on the

#'   helper function).

#'

#' @examples

#' library(dplyr)

#' library(broom)

#'

#' adrs_f <- tern_ex_adrs %>%

#'   filter(PARAMCD == "BESRSPI") %>%

#'   filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%

#'   mutate(

#'     Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),

#'     RACE = factor(RACE),

#'     SEX = factor(SEX)

#'   )

#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")

#' mod1 <- fit_logistic(

#'   data = adrs_f,

#'   variables = list(

#'     response = "Response",

#'     arm = "ARMCD",

#'     covariates = c("AGE", "RACE")

#'   )

#' )

#' mod2 <- fit_logistic(

#'   data = adrs_f,

#'   variables = list(

#'     response = "Response",

#'     arm = "ARMCD",

#'     covariates = c("AGE", "RACE"),

#'     interaction = "AGE"

#'   )

#' )

#'

#' @name h_logistic_regression

NULL

#' @describeIn h_logistic_regression Helper function to extract interaction variable names from a fitted

#'   model assuming only one interaction term.

#'

#' @return Vector of names of interaction variables.

#'

#' @export

h_get_interaction_vars <- function(fit_glm) {

}

#' @describeIn h_logistic_regression Helper function to get the right coefficient name from the

#'   interaction variable names and the given levels. The main value here is that the order

#'   of first and second variable is checked in the `interaction_vars` input.

#'

#' @param interaction_vars (`character` of length 2)\cr interaction variable names.

#' @param first_var_with_level (`character` of length 2)\cr the first variable name with

#'   the interaction level.

#' @param second_var_with_level (`character` of length 2)\cr the second variable name with

#'   the interaction level.

#'

#' @return Name of coefficient.

#'

#' @export

h_interaction_coef_name <- function(interaction_vars,

                                    first_var_with_level,

                                    second_var_with_level) {

  }

}

#' @describeIn h_logistic_regression Helper function to calculate the odds ratio estimates

#'   for the case when both the odds ratio and the interaction variable are categorical.

#'

#' @param odds_ratio_var (`string`)\cr the odds ratio variable.

#' @param interaction_var (`string`)\cr the interaction variable.

#'

#' @return Odds ratio.

#'

#' @export

h_or_cat_interaction <- function(odds_ratio_var,

                                 interaction_var,

                                 fit_glm,

                                 conf_level = 0.95) {

        )

        )

      }

      } else {

      }

    }

  }

}

#' @describeIn h_logistic_regression Helper function to calculate the odds ratio estimates

#'   for the case when either the odds ratio or the interaction variable is continuous.

#'

#' @param at (`NULL` or `numeric`)\cr optional values for the interaction variable. Otherwise

#'   the median is used.

#'

#' @return Odds ratio.

#'

#' @note We don't provide a function for the case when both variables are continuous because

#'   this does not arise in this table, as the treatment arm variable will always be involved

#'   and categorical.

#'

#' @export

h_or_cont_interaction <- function(odds_ratio_var,

                                  interaction_var,

                                  fit_glm,

                                  at = NULL,

                                  conf_level = 0.95) {

  }

    }

          )

          )

        }

        } else {

        }

      }

    }

  } else {

        )

        )

      }

      } else {

      }

    }

  }

}

#' @describeIn h_logistic_regression Helper function to calculate the odds ratio estimates

#'   in case of an interaction. This is a wrapper for [h_or_cont_interaction()] and

#'   [h_or_cat_interaction()].

#'

#' @return Odds ratio.

#'

#' @export

h_or_interaction <- function(odds_ratio_var,

                             interaction_var,

                             fit_glm,

                             at = NULL,

                             conf_level = 0.95) {

    )

    )

  } else {

  }

}

#' @describeIn h_logistic_regression Helper function to construct term labels from simple terms and the table

#'   of numbers of patients.

#'

#' @param terms (`character`)\cr simple terms.

#' @param table (`table`)\cr table containing numbers for terms.

#'

#' @return Term labels containing numbers of patients.

#'

#' @export

h_simple_term_labels <- function(terms,

                                 table) {

}

#' @describeIn h_logistic_regression Helper function to construct term labels from interaction terms and the table

#'   of numbers of patients.

#'

#' @param terms1 (`character`)\cr terms for first dimension (rows).

#' @param terms2 (`character`)\cr terms for second dimension (rows).

#' @param any (`flag`)\cr whether any of `term1` and `term2` can be fulfilled to count the

#'   number of patients. In that case they can only be scalar (strings).

#'

#' @return Term labels containing numbers of patients.

#'

#' @export

h_interaction_term_labels <- function(terms1,

                                      terms2,

                                      table,

                                      any = FALSE) {

      # Note that we double count in the initial sum the cell [terms1, terms2], therefore subtract.

    )

  } else {

  }

}

#' @describeIn h_logistic_regression Helper function to tabulate the main effect

#'   results of a (conditional) logistic regression model.

#'

#' @return Tabulated main effect results from a logistic regression model.

#'

#' @examples

#' h_glm_simple_term_extract("AGE", mod1)

#' h_glm_simple_term_extract("ARMCD", mod1)

#'

#' @export

h_glm_simple_term_extract <- function(x, fit_glm) {

  } else {

  }

  # Make sure x is not an interaction term.

    } else {

      # We just fill in here with the `term` itself as we don't have the data available.

    }

  } else {

    # The reason is that we don't have the original data set in the `clogit` object

    # and therefore cannot determine the `x_numbers` here.

    )

    }

  }

  } else {

  }

  )]

}

#' @describeIn h_logistic_regression Helper function to tabulate the interaction term

#'   results of a logistic regression model.

#'

#' @return Tabulated interaction term results from a logistic regression model.

#'

#' @examples

#' h_glm_interaction_extract("ARMCD:AGE", mod2)

#'

#' @export

h_glm_interaction_extract <- function(x, fit_glm) {

  # Only take two-way interaction

  # Only consider simple case: first variable in interaction is arm, a categorical variable

    )

    )

  }